Unit 3: Unsupervised Learning

Overview

This unit explores unsupervised learning techniques for discovering hidden patterns and structures in unlabeled data.

Key Topics:

• Clustering algorithms (K-Means, Hierarchical, DBSCAN)
• Dimensionality reduction (PCA, t-SNE)
• Anomaly detection
• Feature extraction
• Real-world applications

Learning Outcomes:

• Apply clustering algorithms to segment data
• Use dimensionality reduction for feature extraction and visualization
• Determine optimal number of clusters
• Detect anomalies in datasets
• Interpret and visualize unsupervised learning results

📖 Lecture Content

Lecture 3.1: Clustering Fundamentals

• K-Means clustering algorithm
• Elbow method and silhouette analysis
• Hierarchical clustering
• Linkage methods

Lecture 3.2: Advanced Clustering

• DBSCAN (Density-Based Clustering)
• Gaussian Mixture Models
• Cluster validation
• Handling different cluster shapes

Lecture 3.3: Dimensionality Reduction

• Principal Component Analysis (PCA)
• t-SNE for visualization
• Feature selection vs Feature extraction
• Explained variance

Lecture 3.4: Applications & Anomaly Detection

• Real-world clustering applications
• Customer segmentation
• Image compression
• Anomaly and outlier detection

🧪 Associated Practicals

• Practical 6: K-Means Clustering
• Practical 7: Hierarchical Clustering & Dendrogram Analysis
• Practical 8: PCA for Dimensionality Reduction

✅ Study Checklist

• Implement K-Means clustering
• Determine optimal number of clusters
• Use hierarchical clustering
• Apply DBSCAN for density-based clustering
• Perform PCA dimensionality reduction
• Create effective visualizations
• Detect anomalies in datasets

📚 Key Techniques

💾 Resources

• Clustering Guide
• PCA Explained
• Dimensionality Reduction Reference

📝 Assessment

• Weekly Test 3 (WT3) - Unit 3 concepts
• Class Test 1 (CT1) - Units 1-2
• Class Test 2 (CT2) - Units 3-4

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